Instructions to use dongguanting/Agent-Reflex-8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dongguanting/Agent-Reflex-8B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="dongguanting/Agent-Reflex-8B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dongguanting/Agent-Reflex-8B")
model = AutoModelForCausalLM.from_pretrained("dongguanting/Agent-Reflex-8B", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dongguanting/Agent-Reflex-8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dongguanting/Agent-Reflex-8B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dongguanting/Agent-Reflex-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dongguanting/Agent-Reflex-8B

SGLang

How to use dongguanting/Agent-Reflex-8B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "dongguanting/Agent-Reflex-8B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dongguanting/Agent-Reflex-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "dongguanting/Agent-Reflex-8B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dongguanting/Agent-Reflex-8B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dongguanting/Agent-Reflex-8B with Docker Model Runner:
```
docker model run hf.co/dongguanting/Agent-Reflex-8B
```

Agent-Reflex-8B / README.md

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---
license: mit
base_model: Qwen/Qwen3-8B
library_name: transformers
pipeline_tag: text-generation
tags:
- agent
- tool-use
- function-calling
- reflection
- reinforcement-learning
- arxiv:2610.17953
datasets:
- dongguanting/Agent-Reflex-SFT-54K
- dongguanting/Agent-Reflex-RL-2K
---
# Agent-Reflex-8B

[![Paper](https://img.shields.io/badge/Paper-arXiv-b5212f.svg?logo=arxiv)](https://arxiv.org/abs/2610.17953)
[![HF Paper](https://img.shields.io/badge/🤗%20Paper-2610.17953-ffc107.svg)](https://huggingface.co/papers/2610.17953)
[![GitHub](https://img.shields.io/badge/GitHub-Agent--Reflex-181717.svg?logo=github)](https://github.com/RUC-NLPIR/Agent-Reflex)
[![Collection](https://img.shields.io/badge/🤗%20Collection-Agent--Reflex-ffc107.svg)](https://huggingface.co/collections/dongguanting/agent-reflex)
[![SFT Data](https://img.shields.io/badge/Data-SFT--54K-ffc107.svg?logo=huggingface)](https://huggingface.co/datasets/dongguanting/Agent-Reflex-SFT-54K)
[![RL Data](https://img.shields.io/badge/Data-RL--2K-ffc107.svg?logo=huggingface)](https://huggingface.co/datasets/dongguanting/Agent-Reflex-RL-2K)

**Paper:** [arXiv:2610.17953](https://arxiv.org/abs/2610.17953) · [Hugging Face Papers](https://huggingface.co/papers/2610.17953)

Agent-Reflex-8B is Qwen3-8B trained with **Agent-Reflex**, a framework that elicits proactive reflection in tool-using agents: noticing noisy tool feedback and user corrections, and recovering from them.

Training: Qwen3-8B → **ReSFT** (54,663 reflective trajectories, 5,127 steps) → **ORPO** (noisy agentic RL with skill-guided on-policy distillation). Skills are used only during training; the model runs skill-free.

## Results

| Model | VitaBench | τ²-Bench | BFCL V4 | ACEBench | ToolSandbox | **Overall** | AgentNoiseBench |
| --- | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| Qwen3-8B | 11.4 | 26.2 | 40.4 | 54.0 | 69.1 | 40.2 | 12.3 |
| EnvScaler-8B | 15.8 | 37.9 | 47.6 | 72.5 | 80.5 | 50.9 | 18.2 |
| Agent-World-8B | 14.5 | 54.6 | 51.4 | 72.0 | 75.6 | 53.6 | – |
| **Agent-Reflex-8B** | **17.5** | **62.1** | **52.9** | **78.0** | **82.5** | **58.6** | **29.1** |

Each benchmark is scored with its official metric. Overall is the unweighted mean of the five long-horizon tool-use benchmarks (Table 1 of the paper); AgentNoiseBench is the mean over its τ²-Bench and VitaBench noise settings (Table 2).

## Usage

Serve with vLLM (native function calling, thinking on), as in our evaluation:

```bash
vllm serve dongguanting/Agent-Reflex-8B --served-model-name Qwen/Qwen3-8B \
  --tensor-parallel-size 8 --max-model-len 40960 \
  --enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser deepseek_r1
```

The full evaluation suite (τ²-Bench, BFCL V4, ACEBench, VitaBench, AgentNoiseBench, ToolSandbox) is in the [GitHub repo](https://github.com/RUC-NLPIR/Agent-Reflex/tree/main/evaluation).

## Citation

```bibtex
@misc{dong2026agentreflex,
  title={Agent-Reflex: Teaching Language Agents to Act Reflectively},
  author={Guanting Dong and Xinyu Yang and Jiajie Jin and Yuyang Hu and Xiaoshuai Song and Yifei Chen and Xiaoxi Li and Jiejun Tan and Yutao Zhu and Tat-Seng Chua and Ji-Rong Wen and Zhicheng Dou},
  year={2026},
  eprint={2610.17953},
  archivePrefix={arXiv},
  primaryClass={cs.CL},
  url={https://arxiv.org/abs/2610.17953},
}
```

	---
	license: mit
	base_model: Qwen/Qwen3-8B
	library_name: transformers
	pipeline_tag: text-generation
	tags:
	- agent
	- tool-use
	- function-calling
	- reflection
	- reinforcement-learning
	- arxiv:2610.17953
	datasets:
	- dongguanting/Agent-Reflex-SFT-54K
	- dongguanting/Agent-Reflex-RL-2K
	---

	# Agent-Reflex-8B

	[![Paper](https://img.shields.io/badge/Paper-arXiv-b5212f.svg?logo=arxiv)](https://arxiv.org/abs/2610.17953)
	[![HF Paper](https://img.shields.io/badge/🤗%20Paper-2610.17953-ffc107.svg)](https://huggingface.co/papers/2610.17953)
	[![GitHub](https://img.shields.io/badge/GitHub-Agent--Reflex-181717.svg?logo=github)](https://github.com/RUC-NLPIR/Agent-Reflex)
	[![Collection](https://img.shields.io/badge/🤗%20Collection-Agent--Reflex-ffc107.svg)](https://huggingface.co/collections/dongguanting/agent-reflex)
	[![SFT Data](https://img.shields.io/badge/Data-SFT--54K-ffc107.svg?logo=huggingface)](https://huggingface.co/datasets/dongguanting/Agent-Reflex-SFT-54K)
	[![RL Data](https://img.shields.io/badge/Data-RL--2K-ffc107.svg?logo=huggingface)](https://huggingface.co/datasets/dongguanting/Agent-Reflex-RL-2K)

	Paper: [arXiv:2610.17953](https://arxiv.org/abs/2610.17953) · [Hugging Face Papers](https://huggingface.co/papers/2610.17953)

	Agent-Reflex-8B is Qwen3-8B trained with Agent-Reflex, a framework that elicits proactive reflection in tool-using agents: noticing noisy tool feedback and user corrections, and recovering from them.

	Training: Qwen3-8B → ReSFT (54,663 reflective trajectories, 5,127 steps) → ORPO (noisy agentic RL with skill-guided on-policy distillation). Skills are used only during training; the model runs skill-free.

	## Results

	\| Model \| VitaBench \| τ²-Bench \| BFCL V4 \| ACEBench \| ToolSandbox \| Overall \| AgentNoiseBench \|
	\| --- \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \| :---: \|
	\| Qwen3-8B \| 11.4 \| 26.2 \| 40.4 \| 54.0 \| 69.1 \| 40.2 \| 12.3 \|
	\| EnvScaler-8B \| 15.8 \| 37.9 \| 47.6 \| 72.5 \| 80.5 \| 50.9 \| 18.2 \|
	\| Agent-World-8B \| 14.5 \| 54.6 \| 51.4 \| 72.0 \| 75.6 \| 53.6 \| – \|
	\| Agent-Reflex-8B \| 17.5 \| 62.1 \| 52.9 \| 78.0 \| 82.5 \| 58.6 \| 29.1 \|

	Each benchmark is scored with its official metric. Overall is the unweighted mean of the five long-horizon tool-use benchmarks (Table 1 of the paper); AgentNoiseBench is the mean over its τ²-Bench and VitaBench noise settings (Table 2).

	## Usage

	Serve with vLLM (native function calling, thinking on), as in our evaluation:

	```bash
	vllm serve dongguanting/Agent-Reflex-8B --served-model-name Qwen/Qwen3-8B \
	--tensor-parallel-size 8 --max-model-len 40960 \
	--enable-auto-tool-choice --tool-call-parser hermes --reasoning-parser deepseek_r1
	```

	The full evaluation suite (τ²-Bench, BFCL V4, ACEBench, VitaBench, AgentNoiseBench, ToolSandbox) is in the [GitHub repo](https://github.com/RUC-NLPIR/Agent-Reflex/tree/main/evaluation).

	## Citation

	```bibtex
	@misc{dong2026agentreflex,
	title={Agent-Reflex: Teaching Language Agents to Act Reflectively},
	author={Guanting Dong and Xinyu Yang and Jiajie Jin and Yuyang Hu and Xiaoshuai Song and Yifei Chen and Xiaoxi Li and Jiejun Tan and Yutao Zhu and Tat-Seng Chua and Ji-Rong Wen and Zhicheng Dou},
	year={2026},
	eprint={2610.17953},
	archivePrefix={arXiv},
	primaryClass={cs.CL},
	url={https://arxiv.org/abs/2610.17953},
	}
	```